Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Accurate estimation and robust modelling of translation dynamics at codon resolution

Movie Recommendation System using the MovieLens dataset

Evaluate various supervised learning methods to predict cohesive energies of solids (kernel ridge regression is the best)

glmnet for python

A Genetic Algorithm (GA) / Discrete Particle Swarm Optimization/ Hybrid (GA-PSO) for nuclear fuel optimization using ML surrogates (DNN, KNN, Random Forest, Ridge) and OpenMC. Optimizes fuel loading patterns for a target k-eff and minimal Power Peaking Factor (PPF).

For quick search

Drop-in replacement of sklearn's Linear Regression with coefficients constraints

Applied Machine Learning

Use Ridge Regression and Lasso Regression in prostate cancer data

Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data

Eye gazing point tracker with lower cost webcam with simple calibration, monitor & camera selection, and a transparent on-screen overlay. (2024.09)

Powerful XRP price forecasting using public data. Stacking ensemble (Bi-GRU/LSTM/CNN-LSTM + LightGBM/XGBoost, RidgeR). Fuses market OHLCV (CCXT), news sentiment & top50 whale activity. No API keys or signups. Easy setup. CPU/GPU-ready. Multi-horizon single run forecasting. Backtests + Predictions visuals: plot_charts & in-depth tensorboard dash

Implementation of some Machine Learning Algorithms from scratch

Python implementations of semiparametric statistical techniques.

The Cancer Genome Atlas Pan-Cancer analysis project

Nested Cross-Validation for Bayesian Optimized Linear Regularization

OLS (twoway clustered standard errors), Imperfect Multicollinearity (Ridge and PCA), ARMA(p,q) with Bootstrap